OpenRaiser/Intern-Atlas huggingface dataset:
JRDB-Reasoning: A Difficulty-Graded Benchmark for Visual Reasoning in Robotics
Venue: arXiv cs.CV 2025
Year: 2025

Recent advances in Vision-Language Models (VLMs) and large language models (LLMs) have greatly enhanced visual reasoning, a key capability for embodied AI agents like robots. However, existing visual reasoning benchmarks often suffer from several limitations: they lack a clear definition of reasoning complexity, offer have no control to generate questions over varying difficulty and task customization, and fail to provide structured, step-by-step reasoning annotations (workflows). To bridge these gaps, we formalize reasoning complexity, introduce an adaptive query engine that generates customizable questions of varying complexity with detailed intermediate annotations, and extend the JRDB dataset with human-object interaction and geometric relationship annotations to create JRDB-Reasoning, a benchmark tailored for visual reasoning in human-crowded environments. Our engine and benchmark enable fine-grained evaluation of visual reasoning frameworks and dynamic assessment of visual-language models across reasoning levels.
Influence Maximization in Multi-layer Social Networks Based on Differentiated Graph Embeddings
Venue: arXiv cs.SI 2025
Year: 2025

Identifying influential nodes is crucial in social network analysis. Existing methods often neglect local opinion leader tendencies, resulting in overlapping influence ranges for seed nodes. Furthermore, approaches based on vanilla graph neural networks (GNNs) struggle to effectively aggregate influence characteristics during message passing, particularly with varying influence intensities. Current techniques also fail to adequately address the multi-layer nature of social networks and node heterogeneity. To address these issues, this paper proposes Inf-MDE, a novel multi-layer influence maximization method leveraging differentiated graph embedding. Inf-MDE models social relationships using a multi-layer network structure. The model extracts a self-influence propagation subgraph to eliminate the representation bias between node embeddings and propagation dynamics. Additionally, Inf-MDE incorporates an adaptive local influence aggregation mechanism within its GNN design. This mechanism dynamically adjusts influence feature aggregation during message passing based on local context and influence intensity, enabling it to effectively capture both inter-layer propagation heterogeneity and intra-layer diffusion dynamics. Extensive experiments across four distinct multi-layer social network datasets demonstrate that Inf-MDE significantly outperforms state-of-the-art methods.
Inductive Bias Extraction and Matching for LLM Prompts
Venue: arXiv cs.CL 2025
Year: 2025

The active research topic of prompt engineering makes it evident that LLMs are sensitive to small changes in prompt wording. A portion of this can be ascribed to the inductive bias that is present in the LLM. By using an LLM's output as a portion of its prompt, we can more easily create satisfactory wording for prompts. This has the effect of creating a prompt that matches the inductive bias in model. Empirically, we show that using this Inductive Bias Extraction and Matching strategy improves LLM Likert ratings used for classification by up to 19% and LLM Likert ratings used for ranking by up to 27%.
Improving Learning of New Diseases through Knowledge-Enhanced Initialization for Federated Adapter Tuning
Venue: arXiv cs.LG 2025
Year: 2025

In healthcare, federated learning (FL) is a widely adopted framework that enables privacy-preserving collaboration among medical institutions. With large foundation models (FMs) demonstrating impressive capabilities, using FMs in FL through cost-efficient adapter tuning has become a popular approach. Given the rapidly evolving healthcare environment, it is crucial for individual clients to quickly adapt to new tasks or diseases by tuning adapters while drawing upon past experiences. In this work, we introduce Federated Knowledge-Enhanced Initialization (FedKEI), a novel framework that leverages cross-client and cross-task transfer from past knowledge to generate informed initializations for learning new tasks with adapters. FedKEI begins with a global clustering process at the server to generalize knowledge across tasks, followed by the optimization of aggregation weights across clusters (inter-cluster weights) and within each cluster (intra-cluster weights) to personalize knowledge transfer for each new task. To facilitate more effective learning of the inter- and intra-cluster weights, we adopt a bi-level optimization scheme that collaboratively learns the global intra-cluster weights across clients and optimizes the local inter-cluster weights toward each client's task objective. Extensive experiments on three benchmark datasets of different modalities, including dermatology, chest X-rays, and retinal OCT, demonstrate FedKEI's advantage in adapting to new diseases compared to state-of-the-art methods.
Beyond Self-Regulated Learning Processes: Unveiling Hidden Tactics in Generative AI-Assisted Writing
Venue: arXiv cs.HC 2025
Year: 2025

The integration of Generative AI (GenAI) into education is reshaping how students learn, making self-regulated learning (SRL) - the ability to plan, monitor, and adapt one's learning - more important than ever. To support learners in these new contexts, it is essential to understand how SRL unfolds during interaction with GenAI tools. Learning analytics offers powerful techniques for analyzing digital trace data to infer SRL behaviors. However, existing approaches often assume SRL processes are linear, segmented, and non-overlapping-assumptions that overlook the dynamic, recursive, and non-linear nature of real-world learning. We address this by conceptualizing SRL as a layered system: observable learning patterns reflect hidden tactics (short, purposeful action states), which combine into broader SRL strategies. Using Hidden Markov Models (HMMs), we analyzed trace data from higher education students engaged in GenAI-assisted academic writing. We identified three distinct groups of learners, each characterized by different SRL strategies. These groups showed significant differences in performance, indicating that students' use of different SRL strategies in GenAI-assisted writing led to varying task outcomes. Our findings advance the methodological toolkit for modeling SRL and inform the design of adaptive learning technologies that more effectively support learners in GenAI-enhanced educational environments.
Beyond Semantic Understanding: Preserving Collaborative Frequency Components in LLM-based Recommendation
Venue: arXiv cs.CL 2025
Year: 2025

Recommender systems in concert with Large Language Models (LLMs) present promising avenues for generating semantically-informed recommendations. However, LLM-based recommenders exhibit a tendency to overemphasize semantic correlations within users' interaction history. When taking pretrained collaborative ID embeddings as input, LLM-based recommenders progressively weaken the inherent collaborative signals as the embeddings propagate through LLM backbones layer by layer, as opposed to traditional Transformer-based sequential models in which collaborative signals are typically preserved or even enhanced for state-of-the-art performance. To address this limitation, we introduce FreLLM4Rec, an approach designed to balance semantic and collaborative information from a spectral perspective. Item embeddings that incorporate both semantic and collaborative information are first purified using a Global Graph Low-Pass Filter (G-LPF) to preliminarily remove irrelevant high-frequency noise. Temporal Frequency Modulation (TFM) then actively preserves collaborative signal layer by layer. Note that the collaborative preservation capability of TFM is theoretically guaranteed by establishing a connection between the optimal but hard-to-implement local graph fourier filters and the suboptimal yet computationally efficient frequency-domain filters. Extensive experiments on four benchmark datasets demonstrate that FreLLM4Rec successfully mitigates collaborative signal attenuation and achieves competitive performance, with improvements of up to 8.00\% in NDCG@10 over the best baseline. Our findings provide insights into how LLMs process collaborative information and offer a principled approach for improving LLM-based recommendation systems.
BERTector: An Intrusion Detection Framework Constructed via Joint-dataset Learning Based on Language Model
Venue: arXiv cs.CR 2025
Year: 2025

Intrusion detection systems (IDS) are widely used to maintain the stability of network environments, but still face restrictions in generalizability due to the heterogeneity of network traffics. In this work, we propose BERTector, a new framework of joint-dataset learning for IDS based on BERT. BERTector integrates three key components: NSS-Tokenizer for traffic-aware semantic tokenization, supervised fine-tuning with a hybrid dataset, and low-rank adaptation for efficient fine-tuning. Experiments show that BERTector achieves state-of-the-art detection accuracy, strong generalizability, and excellent robustness. BERTector achieves the highest accuracy of 99.28% on NSL-KDD and reaches the average 80% detection success rate against four perturbations. These results establish a unified and efficient solution for modern IDS in complex and dynamic network environments.
Near-realtime Earth Observation Via Starlink LEO Satellite Constellation
Venue: arXiv cs.NI 2025
Year: 2025

Earth observation (EO) satellites in Low Earth Orbit (LEO) are collecting vast amounts of data, which are invaluable for applications such as monitoring forest fires. However, data downloading from EO satellites faces significant challenges due to the limited number of ground stations and the brief communication windows with them. Conversely, emerging LEO constellations like Starlink have enabled continuous connectivity and revolutionized access for ordinary users globally, who can connect via a simple satellite dish. In this paper, we study the feasibility of supporting EO satellites with Starlink satellite infrastructure and introduce a novel data delivery system, designated as "Starlink Space User" (SSU), for relaying data from observation satellites. SSU treats EO satellites as space users of Starlink, facilitating efficient data transfer to Earth. At the core of SSU is a novel class of algorithms designed for link and PoP selection, as well as system scheduling optimization, that operate effectively atop Starlink's proprietary infrastructure. We assess the performance of SSU using trace-driven simulations alongside real-world Starlink performance measurements. Our results demonstrate that the proposed Starlink-aided design can significantly reduce the median backlog (data not delivered) per satellite.
Glo-UMF: A Unified Multi-model Framework for Automated Morphometry of Glomerular Ultrastructural Characterization
Venue: arXiv cs.CV 2025
Year: 2025

Background and Objective: To address the inability of single-model architectures to perform simultaneous analysis of complex glomerular ultrastructures, we developed Glo-UMF, a unified multi-model framework integrating segmentation, classification, and detection to systematically quantify key ultrastructural features. Methods: Glo-UMF decouples quantification tasks by constructing three dedicated deep models: an ultrastructure segmentation model, a glomerular filtration barrier (GFB) region classification model, and an electron-dense deposits (EDD) detection model. Their outputs are integrated through a post-processing workflow with adaptive GFB cropping and measurement location screening, enhancing measurement reliability and providing comprehensive quantitative results that overcome the limitations of traditional grading. Results: Trained on 372 electron microscopy images, Glo-UMF enables simultaneous quantification of glomerular basement membrane (GBM) thickness, the degree of foot process effacement (FPE), and EDD location. In 115 test cases spanning 9 renal pathological types, the automated quantification results showed strong agreement with pathological reports, with an average processing time of 4.23$\pm$0.48 seconds per case on a CPU environment. Conclusions: The modular design of Glo-UMF allows for flexible extensibility, supporting the joint quantification of multiple features. This framework ensures robust generalization and clinical applicability, demonstrating significant potential as an efficient auxiliary tool in glomerular pathological analysis.
What to Ask Next? Probing the Imaginative Reasoning of LLMs with TurtleSoup Puzzles
Venue: arXiv cs.AI 2025
Year: 2025

We investigate the capacity of Large Language Models (LLMs) for imaginative reasoning--the proactive construction, testing, and revision of hypotheses in information-sparse environments. Existing benchmarks, often static or focused on social deduction, fail to capture the dynamic, exploratory nature of this reasoning process. To address this gap, we introduce a comprehensive research framework based on the classic "Turtle Soup" game, integrating a benchmark, an agent, and an evaluation protocol. We present TurtleSoup-Bench, the first large-scale, bilingual, interactive benchmark for imaginative reasoning, comprising 800 turtle soup puzzles sourced from both the Internet and expert authors. We also propose Mosaic-Agent, a novel agent designed to assess LLMs' performance in this setting. To evaluate reasoning quality, we develop a multi-dimensional protocol measuring logical consistency, detail completion, and conclusion alignment. Experiments with leading LLMs reveal clear capability limits, common failure patterns, and a significant performance gap compared to humans. Our work offers new insights into LLMs' imaginative reasoning and establishes a foundation for future research on exploratory agent behavior.
"Here Comes the Makeup Tutorial You Asked For!": Exploring Communication Strategies and Viewer Engagement in Beauty Videos on Rednote
Venue: arXiv cs.HC 2025
Year: 2025

More and more people, especially females, create and view beauty videos covering topics like makeup tutorials and vlogs on social media platforms. Understanding the communication strategies that creators use in these videos and how they affect viewers' engagement can help spread beauty knowledge. By coding 352 beauty videos in Rednote, this study presents a comprehensive taxonomy of communication strategies used by the creators, such as using home as the video background and displaying makeup effects when starting the narrative at the beginning. We further label and computationally classify six categories of comments that reveal viewers' engagement with beauty videos. The regression analyses reveal the effects of beauty video communication strategies on viewers' engagement; for example, calling viewers to take action at the end tends to attract more comments that debate the product's efficacy. We discuss insights into fostering the creation of beauty videos and the communication of beauty knowledge.
Contrast Sensitivity in Multimodal Large Language Models: A Psychophysics-Inspired Evaluation
Venue: arXiv cs.CV 2025
Year: 2025

Understanding how Multimodal Large Language Models (MLLMs) process low-level visual features is critical for evaluating their perceptual abilities and has not been systematically characterized. Inspired by human psychophysics, we introduce a behavioural method for estimating the Contrast Sensitivity Function (CSF) in MLLMs by treating them as end-to-end observers. Models are queried with structured prompts while viewing noise-based stimuli filtered at specific spatial frequencies. Psychometric functions are derived from the binary verbal responses, and contrast thresholds (and CSFs) are obtained without relying on internal activations or classifier-based proxies. Our results reveal that some models resemble human CSFs in shape or scale, but none capture both. We also find that CSF estimates are highly sensitive to prompt phrasing, indicating limited linguistic robustness. Finally, we show that CSFs predict model performance under frequency-filtered and adversarial conditions. These findings highlight systematic differences in frequency tuning across MLLMs and establish CSF estimation as a scalable diagnostic tool for multimodal perception.
Large Language Models for Summarizing Czech Historical Documents and Beyond
Venue: arXiv cs.CL 2025
Year: 2025

Text summarization is the task of shortening a larger body of text into a concise version while retaining its essential meaning and key information. While summarization has been significantly explored in English and other high-resource languages, Czech text summarization, particularly for historical documents, remains underexplored due to linguistic complexities and a scarcity of annotated datasets. Large language models such as Mistral and mT5 have demonstrated excellent results on many natural language processing tasks and languages. Therefore, we employ these models for Czech summarization, resulting in two key contributions: (1) achieving new state-of-the-art results on the modern Czech summarization dataset SumeCzech using these advanced models, and (2) introducing a novel dataset called Posel od Čerchova for summarization of historical Czech documents with baseline results. Together, these contributions provide a great potential for advancing Czech text summarization and open new avenues for research in Czech historical text processing.
Improving Generative Cross-lingual Aspect-Based Sentiment Analysis with Constrained Decoding
Venue: arXiv cs.CL 2025
Year: 2025

While aspect-based sentiment analysis (ABSA) has made substantial progress, challenges remain for low-resource languages, which are often overlooked in favour of English. Current cross-lingual ABSA approaches focus on limited, less complex tasks and often rely on external translation tools. This paper introduces a novel approach using constrained decoding with sequence-to-sequence models, eliminating the need for unreliable translation tools and improving cross-lingual performance by 5\% on average for the most complex task. The proposed method also supports multi-tasking, which enables solving multiple ABSA tasks with a single model, with constrained decoding boosting results by more than 10\%. We evaluate our approach across seven languages and six ABSA tasks, surpassing state-of-the-art methods and setting new benchmarks for previously unexplored tasks. Additionally, we assess large language models (LLMs) in zero-shot, few-shot, and fine-tuning scenarios. While LLMs perform poorly in zero-shot and few-shot settings, fine-tuning achieves competitive results compared to smaller multilingual models, albeit at the cost of longer training and inference times. We provide practical recommendations for real-world applications, enhancing the understanding of cross-lingual ABSA methodologies. This study offers valuable insights into the strengths and limitations of cross-lingual ABSA approaches, advancing the state-of-the-art in this challenging research domain.
eMamba: Efficient Acceleration Framework for Mamba Models in Edge Computing
Venue: arXiv cs.LG 2025
Year: 2025

State Space Model (SSM)-based machine learning architectures have recently gained significant attention for processing sequential data. Mamba, a recent sequence-to-sequence SSM, offers competitive accuracy with superior computational efficiency compared to state-of-the-art transformer models. While this advantage makes Mamba particularly promising for resource-constrained edge devices, no hardware acceleration frameworks are currently optimized for deploying it in such environments. This paper presents eMamba, a comprehensive end-to-end hardware acceleration framework explicitly designed for deploying Mamba models on edge platforms. eMamba maximizes computational efficiency by replacing complex normalization layers with lightweight hardware-aware alternatives and approximating expensive operations, such as SiLU activation and exponentiation, considering the target applications. Then, it performs an approximation-aware neural architecture search (NAS) to tune the learnable parameters used during approximation. Evaluations with Fashion-MNIST, CIFAR-10, and MARS, an open-source human pose estimation dataset, show eMamba achieves comparable accuracy to state-of-the-art techniques using 1.63-19.9$\times$ fewer parameters. In addition, it generalizes well to large-scale natural language tasks, demonstrating stable perplexity across varying sequence lengths on the WikiText2 dataset. We also quantize and implement the entire eMamba pipeline on an AMD ZCU102 FPGA and ASIC using GlobalFoundries (GF) 22 nm technology. Experimental results show 4.95-5.62$\times$ lower latency and 2.22-9.95$\times$ higher throughput, with 4.77$\times$ smaller area, 9.84$\times$ lower power, and 48.6$\times$ lower energy consumption than baseline solutions while maintaining competitive accuracy.
Clicks Versus Conversion: Choosing a Recommender's Training Objective in E-Commerce
Venue: arXiv cs.IR 2025
Year: 2025

Ranking product recommendations to optimize for a high click-through rate (CTR) or for high conversion, such as add-to-cart rate (ACR) and Order-Submit-Rate (OSR, view-to-purchase conversion) are standard practices in e-commerce. Optimizing for CTR appears like a straightforward choice: Training data (i.e., click data) are simple to collect and often available in large quantities. Additionally, CTR is used far beyond e-commerce, making it a generalist, easily implemented option. ACR and OSR, on the other hand, are more directly linked to a shop's business goals, such as the Gross Merchandise Value (GMV). In this paper, we compare the effects of using either of these objectives using an online A/B test. Among our key findings, we demonstrate that in our shops, optimizing for OSR produces a GMV uplift more than five times larger than when optimizing for CTR, without sacrificing new product discovery. Our results also provide insights into the different feature importances for each of the objectives.
Towards Spatially Consistent Image Generation: On Incorporating Intrinsic Scene Properties into Diffusion Models
Venue: arXiv cs.CV 2025
Year: 2025

Image generation models trained on large datasets can synthesize high-quality images but often produce spatially inconsistent and distorted images due to limited information about the underlying structures and spatial layouts. In this work, we leverage intrinsic scene properties (e.g., depth, segmentation maps) that provide rich information about the underlying scene, unlike prior approaches that solely rely on image-text pairs or use intrinsics as conditional inputs. Our approach aims to co-generate both images and their corresponding intrinsics, enabling the model to implicitly capture the underlying scene structure and generate more spatially consistent and realistic images. Specifically, we first extract rich intrinsic scene properties from a large image dataset with pre-trained estimators, eliminating the need for additional scene information or explicit 3D representations. We then aggregate various intrinsic scene properties into a single latent variable using an autoencoder. Building upon pre-trained large-scale Latent Diffusion Models (LDMs), our method simultaneously denoises the image and intrinsic domains by carefully sharing mutual information so that the image and intrinsic reflect each other without degrading image quality. Experimental results demonstrate that our method corrects spatial inconsistencies and produces a more natural layout of scenes while maintaining the fidelity and textual alignment of the base model (e.g., Stable Diffusion).